Cooperative vehicle localization method based on coprime polarized MIMO systems for internet-of-vehicles

Vehicle position awareness is a kernel technique for the Internet-of-Vehicles (IOV). In this paper, we develop a novel vehicle localization framework, the core of which is to measure the two-dimensional (2D) angle-of-arrival (AOA) of the vehicle via coprime polarized multiple-input multiple-output (MIMO) systems. The transmitting polarized array emit mutual orthogonal training waveforms, and the receiving array pick up the waveforms via matched filters. A parallel factor (PARAFAC) decomposition algorithm is carried out, which first obtains the ambiguous elevation angles corresponding to the transmit array and receive array, and then it estimates the unambiguous elevation angles via the coprime characteristic of the arrays. Thereafter, it achieves the azimuth angles via the vector cross-product technique. Finally, the vehicle position is calculated via the relationship between the MIMO system and the vehicle. The proposed framework is suitable for anonymous vehicle. Numerical results are provided to show the effectiveness of the proposed framework.